Spatio-temporal data on the air pollutant nitrogen dioxide derived from Sentinel satellite for France

Hichem Omrani; Bilel Omrani; Benoit Parmentier; Marco Helbich

doi:10.1016/j.dib.2019.105089

Spatio-temporal data on the air pollutant nitrogen dioxide derived from Sentinel satellite for France

Hichem Omrani, Bilel Omrani, Benoit Parmentier, Marco Helbich

Urban Development & Mobility

Research output: Contribution to journal › Article › peer-review

Abstract

Monitoring of air pollution is an important task in public health. Availability of data is often hindered by the paucity of the ground monitoring station network. We present here a new spatio-temporal dataset collected and processed from the Sentinel 5P remote sensing platform. As an example application, we applied the full workflow to process measurements of nitrogen dioxide (NO2) collected over the territory of mainland France from May 2018 to June 2019. The data stack generated is daily measurements at a 4 × 7 km spatial resolution. The supplementary Python code package used to collect and process the data is made publicly available. The dataset provided in this article is of value for policy-makers and health assessment.

Original language	English
Article number	105089
Journal	Data in Brief
Volume	28
DOIs	https://doi.org/10.1016/j.dib.2019.105089
Publication status	Published - Feb 2020

Keywords

Air pollution
Remote sensing
Monitoring

Access to Document

10.1016/j.dib.2019.105089

http://www.sciencedirect.com/science/article/pii/S2352340919314453

Cite this

@article{d9a809bac8d04c8697945c4e6c16826a,

title = "Spatio-temporal data on the air pollutant nitrogen dioxide derived from Sentinel satellite for France",

abstract = "Monitoring of air pollution is an important task in public health. Availability of data is often hindered by the paucity of the ground monitoring station network. We present here a new spatio-temporal dataset collected and processed from the Sentinel 5P remote sensing platform. As an example application, we applied the full workflow to process measurements of nitrogen dioxide (NO2) collected over the territory of mainland France from May 2018 to June 2019. The data stack generated is daily measurements at a 4 × 7 km spatial resolution. The supplementary Python code package used to collect and process the data is made publicly available. The dataset provided in this article is of value for policy-makers and health assessment.",

keywords = "Air pollution, Remote sensing, Monitoring",

author = "Hichem Omrani and Bilel Omrani and Benoit Parmentier and Marco Helbich",

year = "2020",

month = feb,

doi = "10.1016/j.dib.2019.105089",

language = "English",

volume = "28",

journal = "Data in Brief",

issn = "2352-3409",

publisher = "Elsevier BV",

}

TY - JOUR

T1 - Spatio-temporal data on the air pollutant nitrogen dioxide derived from Sentinel satellite for France

AU - Omrani, Hichem

AU - Omrani, Bilel

AU - Parmentier, Benoit

AU - Helbich, Marco

PY - 2020/2

Y1 - 2020/2

N2 - Monitoring of air pollution is an important task in public health. Availability of data is often hindered by the paucity of the ground monitoring station network. We present here a new spatio-temporal dataset collected and processed from the Sentinel 5P remote sensing platform. As an example application, we applied the full workflow to process measurements of nitrogen dioxide (NO2) collected over the territory of mainland France from May 2018 to June 2019. The data stack generated is daily measurements at a 4 × 7 km spatial resolution. The supplementary Python code package used to collect and process the data is made publicly available. The dataset provided in this article is of value for policy-makers and health assessment.

AB - Monitoring of air pollution is an important task in public health. Availability of data is often hindered by the paucity of the ground monitoring station network. We present here a new spatio-temporal dataset collected and processed from the Sentinel 5P remote sensing platform. As an example application, we applied the full workflow to process measurements of nitrogen dioxide (NO2) collected over the territory of mainland France from May 2018 to June 2019. The data stack generated is daily measurements at a 4 × 7 km spatial resolution. The supplementary Python code package used to collect and process the data is made publicly available. The dataset provided in this article is of value for policy-makers and health assessment.

KW - Air pollution

KW - Remote sensing

KW - Monitoring

UR - https://www.mendeley.com/catalogue/4ff734e3-b515-314d-9060-84c9d82fafb3/

U2 - 10.1016/j.dib.2019.105089

DO - 10.1016/j.dib.2019.105089

M3 - Article

C2 - 31956677

SN - 2352-3409

VL - 28

JO - Data in Brief

JF - Data in Brief

M1 - 105089

ER -

Spatio-temporal data on the air pollutant nitrogen dioxide derived from Sentinel satellite for France

Abstract

Keywords

Access to Document

Other files and links

Cite this